🧠 Fable-5 Premium Dataset

A rigorously cleaned, high-quality supervised fine-tuning (SFT) dataset built from Claude Fable-5 agent traces.

Priorities: Quality > Ease of Access > Quantity

📊 Dataset Overview

📦 Formats Available

This dataset is available in two formats:

1. OpenAI Chat Format — Standard messages array with user/assistant/tool roles. Ready for Axolotl, Unsloth, and OpenAI fine-tuning API.
2. Hugging Face Agent Traces Format — Native HF Agent Traces viewable in Data Studio.

🔗 Sources

🧹 Quality Pipeline

1. Deduplication — SHA-256 content hashing across all sources (cross-source dedup)
2. Structural Validation — Valid message schemas, tool call IDs, proper role sequencing
3. Content Filtering — Remove empty/truncated responses, error-only sessions, placeholders
4. PII Scrubbing — Remove local paths, API keys, environment-specific data
5. Tool Call Validation — Ensure tool calls have matching tool responses
6. Quality Scoring — Multi-dimensional quality metrics

📈 Quality Distribution

🎯 Usage

With Hugging Face Datasets

from datasets import load_dataset

# Load OpenAI Chat format
dataset = load_dataset("saidutta69/fable-5-premium", "openai_chat", split="train")

# Load Agent Traces format
traces = load_dataset("saidutta69/fable-5-premium", "agent_traces", split="train")

With Axolotl

# axolotl config
dataset:
  - path: saidutta69/fable-5-premium
    type: chat_template
    split: train

With Unsloth

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/llama-3-8b",
    max_seq_length=4096,
)

🏗️ Chain-of-Thought (CoT)

• reasoning field — Separate field for models that support explicit thinking tokens
• Embedded <think> tags — CoT merged into assistant content for standard fine-tuning